Glaucoma is one of the leading causes of blindness worldwide. According to a World Health Organization report in 2004 [1], glaucoma is the second leading cause of blindness, with 12.3% of visual impairment globally being attributed to this disease. It has been estimated that more than half of the 70 million glaucoma cases are in Asia [2]. In Singapore, a recent community-based study [3] found that 3.2% of Chinese respondents over the age of 40 were found to have glaucoma. Furthermore, the disease is closely associated with age [4] and according to the Glaucoma Research Foundation [5], a person over the age of 60 is “six times more likely to get glaucoma”. Given the rapidly aging population in many parts of the world, including Singapore, the prevalence of glaucoma is likely to increase.
Characterized by the death of ganglion nerve cells, glaucomatous damage is irreversible and permanent. Furthermore, noticeable loss of visual field is usually only realized on advanced glaucoma damage. However, glaucomatous progression can be significantly slowed or even halted if detected early enough [6]. This motivates the need for mass screening efforts to detect glaucoma at an early stage. However, currently, glaucoma screening is labor intensive as it requires manual grading of the retinal image by experienced professionals. This process is time-consuming and the need for a trained clinician raises the difficulty of conducting mass screening events. Furthermore, it has been reported that the accuracy in the assessment of glaucoma is subject to the experience and training of the observer [7]. Due to the limitations of the current manual method, there is hence a need for the development of an objective and computerized screening system for glaucoma screening.
FIG. 1(a) is a 2D retinal image of an eye which does not exhibit glaucoma, and FIG. 1(d) is a corresponding schematic diagram of the rear of the eye in partial cut-away view. The optic nerve transmits visual information from the eye to the brain and the optic nerve head, also known as the optic disc, is the start of the optic nerve in the retina. The optic disc is a structure by which the nerve fibers leave the retina. Within the optic disc is a depression known as the optic cup.
Glaucomatous optical neuropathy is physiologically characterized by an increased excavation of the optic cup, causing an increase in the size of the optic cup in relation to the optic disc. In FIGS. 1(b) and 1(e), which correspond in meaning respectively to FIGS. 1(a) and 1(d), the physiology of an eye with early glaucoma is illustrated, whereas in FIGS. 1(c) and 1(f), which again correspond in meaning respectively to FIGS. 1(a) and 1(d), the physiology of an eye with late glaucoma is shown. As shown in these figures, there is a loss of ganglion cells and a thinning of the nerve fibers when glaucoma is present. In addition, there is also an increased excavation of the optic cups in the glaucomatous eyes.
A standard feature employed by current methods for the risk assessment of glaucoma in a patient is the measurement of the ratio between the optic cup and the optic disc, more commonly known as the cup to disc ratio (CDR). The CDR is used to provide a quantitative measure of the relative cup to disc size. Typically, if the CDR is high, this represents a higher risk of glaucoma and further examination may be recommended.
To obtain the CDR, it is necessary to isolate the optic disc and the optic cup from each other and from the rest of the retinal image. Previously, many methods have been reported for optic disc detection, but significantly fewer methods have been presented for optic cup detection. Optic cup detection is much more challenging due to the reduced visibility of the optic cup within the optic disc and the high density of vascular architecture traversing the optic cup boundary. One of the earliest reported methods for optic cup detection was based on the discriminatory analysis of color intensity [8]. Further methods for optic cup detection were subsequently developed. For example, pixels within the retinal image were classified based on pixel features generated from stereo color retinal images [9]. Gradient, shape and depth parameters obtained from prior stereoscopic reconstruction were used to derive the energy function for the cup in [10] and a variational level set method based on pixel intensity was used to globally optimize the obtained cup contour in [11].
The pixel color intensity, or pallor, and the associated gradients form the basis of the previously described methods for cup segmentation. When cup pallor is used to determine the size of the cup, there is a potential risk of understating the cup size. This results in an effect known as pallor/cup discrepancy, where the area of pallor lags behind the actual cup size [12]. In addition, when the difference in pallor between the cup and the disc is not particularly visible, especially in early glaucomatous atrophy when the cup can be shallow, the accurate determination of the cup size using pallor alone can be challenging. In addition, many of the methods [9, 10] further depend on the processing of stereoscopic images to obtain the cup boundary.
There is hence a need for a method and system for determining an optic cup boundary which can overcome the above problems.